COVID-19 Stocks

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import warnings
warnings.filterwarnings("ignore")

# yahoo finance data 
import yfinance as yf
yf.pdr_override()

# input
# Online Gaming
title = "COVID-19"
symbols = ['PRGO', 'AMED', 'EHC', 'LHCG', 'CNC', 'TDOC', 'DXCM', 'HOLX', 'BIO', 'DHR', 'TMO', 'ANTM', 'BMY', 'GH', 'IOVA', 'IQV', 'MDT', 'VEEV', 'WST', 'FSPHX', 'BHCFX', 'PRHSX','IHI', 'RYH', 'XLV', 'XHE']
start = '2018-01-01'
end = '2020-06-26'

df = pd.DataFrame()
for s in symbols:
    df[s] = yf.download(s,start,end)['Adj Close']

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from datetime import datetime
from dateutil import relativedelta

d1 = datetime.strptime(start, "%Y-%m-%d")
d2 = datetime.strptime(end, "%Y-%m-%d")
delta = relativedelta.relativedelta(d2,d1)
print('How many years of investing?')
print('%s years' % delta.years)

How many years of investing?
2 years

number_of_years = delta.years

days = (df.index[-1] - df.index[0]).days
days

905

df.head()

	PRGO	AMED	EHC	LHCG	CNC	TDOC	DXCM	HOLX	BIO	DHR	...	MDT	VEEV	WST	FSPHX	BHCFX	PRHSX	IHI	RYH	XLV	XHE
Date
2018-01-02	86.560356	53.790001	49.234493	61.619999	51.294998	36.200001	57.990002	42.869999	241.419998	91.191078	...	77.746536	55.070000	98.237854	19.940559	NaN	63.421398	175.057892	179.344879	79.736702	65.242516
2018-01-03	86.319077	52.689999	48.745792	61.919998	52.215000	36.849998	58.080002	43.490002	250.470001	92.404449	...	79.363701	56.279999	98.356316	20.119801	NaN	64.103729	176.905106	180.951279	80.499641	65.332390
2018-01-04	87.901848	53.049999	50.259811	64.510002	51.250000	37.200001	52.250000	43.770000	248.490005	92.848373	...	79.552849	56.900002	98.326698	20.077627	NaN	63.837891	176.905106	181.246933	80.614067	65.212547
2018-01-05	87.998344	52.889999	50.278976	64.339996	52.744999	37.500000	53.259998	43.980000	249.050003	93.430397	...	80.895760	57.439999	99.560852	20.205027	NaN	64.289825	178.921112	182.804062	81.300713	65.901535
2018-01-08	86.984993	50.509998	47.969620	61.259998	52.459999	36.049999	55.439999	44.419998	251.000000	93.963112	...	80.867371	57.709999	100.123627	20.168125	NaN	63.802444	179.944061	182.784348	81.005074	66.300941

5 rows × 26 columns

df.tail()

	PRGO	AMED	EHC	LHCG	CNC	TDOC	DXCM	HOLX	BIO	DHR	...	MDT	VEEV	WST	FSPHX	BHCFX	PRHSX	IHI	RYH	XLV	XHE
Date
2020-06-19	54.869999	198.580002	63.943573	168.830002	64.220001	201.529999	409.359985	55.160000	451.959991	172.768433	...	93.297523	226.669998	216.589996	30.320000	14.49	87.500000	264.149994	222.500992	100.325005	87.790001
2020-06-22	55.180000	197.600006	63.445885	168.520004	63.099998	204.940002	406.329987	55.209999	456.489990	174.656448	...	92.721306	238.919998	217.800003	30.450001	14.60	87.809998	263.920013	221.979996	99.940002	88.029999
2020-06-23	55.070000	198.309998	62.878510	168.940002	63.770000	198.080002	406.899994	55.500000	451.779999	175.405655	...	92.780907	239.100006	217.539993	30.580000	14.73	88.419998	265.769989	222.949997	100.370003	88.989998
2020-06-24	53.950001	192.000000	60.609032	166.759995	61.880001	197.009995	394.299988	53.669998	432.420013	171.279999	...	88.349998	234.309998	216.509995	29.730000	14.31	86.000000	255.690002	215.949997	97.750000	85.790001
2020-06-25	54.730000	198.649994	60.250690	172.100006	63.360001	201.699997	398.089996	54.570000	437.239990	172.149994	...	88.860001	236.820007	223.360001	30.150000	14.53	87.050003	257.720001	217.919998	98.709999	86.820000

5 rows × 26 columns

plt.figure(figsize=(12,8))
plt.plot(df)
plt.title(title + ' Closing Price')
plt.legend(labels=df.columns)

<matplotlib.legend.Legend at 0x24fe02d2eb8>

# Normalize the data
normalize = (df - df.min())/ (df.max() - df.min())

plt.figure(figsize=(18,12))
plt.plot(normalize)
plt.title(title + ' Stocks Normalize')
plt.legend(labels=normalize.columns)

<matplotlib.legend.Legend at 0x24fe0330cc0>

stock_rets = df.pct_change().dropna()

plt.figure(figsize=(12,8))
plt.plot(stock_rets)
plt.title(title + ' Stocks Returns')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x24fe03aae80>

plt.figure(figsize=(12,8))
plt.plot(stock_rets.cumsum())
plt.title(title + ' Stocks Returns Cumulative Sum')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x24fe046af28>

sns.set(style='ticks')
ax = sns.pairplot(stock_rets, diag_kind='hist')

nplot = len(stock_rets.columns)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

ax = sns.PairGrid(stock_rets)
ax.map_upper(plt.scatter, color='purple')
ax.map_lower(sns.kdeplot, color='blue')
ax.map_diag(plt.hist, bins=30)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

plt.figure(figsize=(10,10))
corr = stock_rets.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns,
            cmap="Reds")

<matplotlib.axes._subplots.AxesSubplot at 0x24ff2c2bcf8>

# Box plot
stock_rets.plot(kind='box',figsize=(24,8))

<matplotlib.axes._subplots.AxesSubplot at 0x24ff749bda0>

rets = stock_rets.dropna()

plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(),alpha = 0.5)

plt.title('Stocks Risk & Returns')
plt.xlabel('Risk')
plt.ylabel('Expected Returns')
plt.grid(which='major')

for label, x, y in zip(rets.columns, rets.std(), rets.mean()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

rets = stock_rets.dropna()
area = np.pi*20.0

sns.set(style='darkgrid')
plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(), s=area)
plt.xlabel("Risk", fontsize=15)
plt.ylabel("Expected Return", fontsize=15)
plt.title("Return vs. Risk for Stocks", fontsize=20)

for label, x, y in zip(rets.columns, rets.std(), rets.mean()) : 
    plt.annotate(label, xy=(x,y), xytext=(50, 0), textcoords='offset points',
                arrowprops=dict(arrowstyle='-', connectionstyle='bar,angle=180,fraction=-0.2'),
                bbox=dict(boxstyle="round", fc="w"))

def annual_risk_return(stock_rets):
    tradeoff = stock_rets.agg(["mean", "std"]).T
    tradeoff.columns = ["Return", "Risk"]
    tradeoff.Return = tradeoff.Return*252
    tradeoff.Risk = tradeoff.Risk * np.sqrt(252)
    return tradeoff

tradeoff = annual_risk_return(stock_rets)
tradeoff

	Return	Risk
PRGO	-0.015248	0.484617
AMED	0.429228	0.474288
EHC	-0.051871	0.365559
LHCG	0.425654	0.426948
CNC	0.040352	0.471479
TDOC	0.740640	0.615577
DXCM	0.818884	0.542420
HOLX	0.236323	0.350276
BIO	0.280522	0.356767
DHR	0.324552	0.292127
TMO	0.266928	0.315727
ANTM	0.082531	0.449037
BMY	0.033065	0.297355
GH	0.847142	0.784935
IOVA	0.848977	0.844912
IQV	0.130354	0.405695
MDT	0.023347	0.320116
VEEV	0.615006	0.452665
WST	0.421413	0.326266
FSPHX	0.164696	0.264559
BHCFX	0.192423	0.268106
PRHSX	0.140757	0.275709
IHI	0.138372	0.284967
RYH	0.085491	0.264726
XLV	0.079399	0.253971
XHE	0.062220	0.282395

import itertools

colors = itertools.cycle(["r", "b", "g"])

tradeoff.plot(x = "Risk", y = "Return", kind = "scatter", figsize = (13,9), s = 20, fontsize = 15, c='g')
for i in tradeoff.index:
    plt.annotate(i, xy=(tradeoff.loc[i, "Risk"]+0.002, tradeoff.loc[i, "Return"]+0.002), size = 15)
plt.xlabel("Annual Risk", fontsize = 15)
plt.ylabel("Annual Return", fontsize = 15)
plt.title("Return vs. Risk for " + title + " Stocks", fontsize = 20)
plt.show()

rest_rets = rets.corr()
pair_value = rest_rets.abs().unstack()
pair_value.sort_values(ascending = False)

XHE    XHE      1.000000
BMY    BMY      1.000000
IHI    IHI      1.000000
PRHSX  PRHSX    1.000000
BHCFX  BHCFX    1.000000
FSPHX  FSPHX    1.000000
WST    WST      1.000000
VEEV   VEEV     1.000000
MDT    MDT      1.000000
IQV    IQV      1.000000
IOVA   IOVA     1.000000
GH     GH       1.000000
ANTM   ANTM     1.000000
XLV    XLV      1.000000
TMO    TMO      1.000000
DHR    DHR      1.000000
BIO    BIO      1.000000
HOLX   HOLX     1.000000
DXCM   DXCM     1.000000
TDOC   TDOC     1.000000
CNC    CNC      1.000000
LHCG   LHCG     1.000000
EHC    EHC      1.000000
AMED   AMED     1.000000
RYH    RYH      1.000000
PRGO   PRGO     1.000000
FSPHX  PRHSX    0.988098
PRHSX  FSPHX    0.988098
BHCFX  PRHSX    0.975807
PRHSX  BHCFX    0.975807
                  ...   
IOVA   WST      0.269274
WST    IOVA     0.269274
IOVA   PRGO     0.266580
PRGO   IOVA     0.266580
TDOC   HOLX     0.258916
HOLX   TDOC     0.258916
CNC    IOVA     0.254692
IOVA   CNC      0.254692
CNC    TDOC     0.245909
TDOC   CNC      0.245909
GH     BMY      0.242537
BMY    GH       0.242537
TDOC   EHC      0.239568
EHC    TDOC     0.239568
GH     TDOC     0.210314
TDOC   GH       0.210314
GH     PRGO     0.201491
PRGO   GH       0.201491
TDOC   PRGO     0.185020
PRGO   TDOC     0.185020
BMY    TDOC     0.166238
TDOC   BMY      0.166238
       ANTM     0.140352
ANTM   TDOC     0.140352
IQV    TDOC     0.137542
TDOC   IQV      0.137542
DXCM   PRGO     0.137146
PRGO   DXCM     0.137146
MDT    TDOC     0.114734
TDOC   MDT      0.114734
Length: 676, dtype: float64

# Normalized Returns Data
Normalized_Value = ((rets[:] - rets[:].min()) /(rets[:].max() - rets[:].min()))
Normalized_Value.head()

	PRGO	AMED	EHC	LHCG	CNC	TDOC	DXCM	HOLX	BIO	DHR	...	MDT	VEEV	WST	FSPHX	BHCFX	PRHSX	IHI	RYH	XLV	XHE
Date
2018-10-05	0.690260	0.562359	0.536978	0.649706	0.513586	0.336857	0.411107	0.684928	0.271427	0.499725	...	0.565751	0.457761	0.544863	0.591899	0.523060	0.585188	0.474131	0.551651	0.554712	0.433003
2018-10-08	0.707617	0.579870	0.485790	0.672598	0.461456	0.226329	0.332200	0.703995	0.209373	0.434754	...	0.503575	0.350733	0.481377	0.545449	0.504099	0.551223	0.426491	0.534070	0.558932	0.390249
2018-10-09	0.637191	0.605416	0.521362	0.664687	0.496914	0.349628	0.369690	0.746034	0.280333	0.555288	...	0.567200	0.432260	0.479968	0.591609	0.549262	0.598717	0.497223	0.568636	0.571010	0.444912
2018-10-10	0.724004	0.509101	0.413736	0.563156	0.466513	0.162076	0.304817	0.624936	0.190669	0.361276	...	0.506642	0.270761	0.365599	0.450244	0.408990	0.436530	0.356251	0.426445	0.426885	0.318880
2018-10-11	0.580602	0.446580	0.454827	0.498729	0.420770	0.377676	0.469028	0.588099	0.208958	0.373264	...	0.467653	0.453715	0.469955	0.527043	0.567226	0.524940	0.438002	0.423063	0.407576	0.448707

5 rows × 26 columns

Normalized_Value.corr()

	PRGO	AMED	EHC	LHCG	CNC	TDOC	DXCM	HOLX	BIO	DHR	...	MDT	VEEV	WST	FSPHX	BHCFX	PRHSX	IHI	RYH	XLV	XHE
PRGO	1.000000	0.330321	0.421733	0.397212	0.351381	0.185020	0.137146	0.323019	0.394771	0.372117	...	0.370139	0.307317	0.312676	0.502238	0.471266	0.513622	0.437217	0.552704	0.510095	0.460672
AMED	0.330321	1.000000	0.519656	0.739781	0.493172	0.384234	0.374164	0.501502	0.435589	0.439176	...	0.392256	0.553329	0.503020	0.604170	0.591088	0.600802	0.547192	0.583548	0.573477	0.547343
EHC	0.421733	0.519656	1.000000	0.590691	0.598567	0.239568	0.376651	0.586990	0.475801	0.546099	...	0.600765	0.397144	0.437905	0.684917	0.676603	0.683284	0.661016	0.722278	0.677864	0.677175
LHCG	0.397212	0.739781	0.590691	1.000000	0.500541	0.280427	0.369352	0.567759	0.435204	0.458314	...	0.491939	0.499758	0.479871	0.622924	0.624295	0.627216	0.588734	0.632329	0.597185	0.605994
CNC	0.351381	0.493172	0.598567	0.500541	1.000000	0.245909	0.394574	0.567670	0.475005	0.579102	...	0.571048	0.463816	0.497678	0.780172	0.729304	0.756161	0.675662	0.754814	0.749223	0.653368
TDOC	0.185020	0.384234	0.239568	0.280427	0.245909	1.000000	0.379764	0.258916	0.298191	0.325537	...	0.114734	0.477715	0.315361	0.382295	0.392995	0.396068	0.323259	0.320719	0.292672	0.380840
DXCM	0.137146	0.374164	0.376651	0.369352	0.394574	0.379764	1.000000	0.461830	0.384244	0.422699	...	0.416064	0.491907	0.387376	0.529738	0.605759	0.545473	0.561182	0.482979	0.445336	0.600303
HOLX	0.323019	0.501502	0.586990	0.567759	0.567670	0.258916	0.461830	1.000000	0.480939	0.660976	...	0.729050	0.523549	0.571468	0.774596	0.787361	0.788606	0.785022	0.791210	0.746226	0.802116
BIO	0.394771	0.435589	0.475801	0.435204	0.475005	0.298191	0.384244	0.480939	1.000000	0.594643	...	0.492226	0.517494	0.531894	0.666729	0.657117	0.676330	0.665213	0.661519	0.661616	0.631179
DHR	0.372117	0.439176	0.546099	0.458314	0.579102	0.325537	0.422699	0.660976	0.594643	1.000000	...	0.674131	0.555986	0.595404	0.794327	0.787094	0.806280	0.845266	0.811404	0.830435	0.757714
TMO	0.367520	0.452512	0.511957	0.456771	0.550337	0.289287	0.395989	0.576608	0.605998	0.764567	...	0.594334	0.558454	0.585442	0.746848	0.746235	0.762318	0.835980	0.772319	0.800791	0.704831
ANTM	0.360116	0.438720	0.585543	0.479001	0.786171	0.140352	0.344115	0.582206	0.471716	0.600962	...	0.629930	0.446765	0.498745	0.788420	0.738368	0.767376	0.703434	0.778565	0.785593	0.664249
BMY	0.380199	0.345322	0.450837	0.360346	0.442385	0.166238	0.278441	0.451035	0.464550	0.576484	...	0.518727	0.401997	0.461805	0.637179	0.621884	0.638797	0.608214	0.653393	0.684434	0.563695
GH	0.201491	0.291030	0.338835	0.337941	0.355004	0.210314	0.367041	0.393455	0.297195	0.319698	...	0.360988	0.400795	0.307824	0.447053	0.499969	0.465724	0.420193	0.410415	0.376313	0.434427
IOVA	0.266580	0.323435	0.295945	0.329814	0.254692	0.301562	0.299718	0.348367	0.294601	0.365089	...	0.336476	0.396784	0.269274	0.479196	0.494499	0.504954	0.442845	0.441325	0.420033	0.459511
IQV	0.430048	0.416765	0.620265	0.510660	0.616209	0.137542	0.443854	0.722497	0.532304	0.677941	...	0.756665	0.498632	0.509864	0.799427	0.812867	0.805533	0.810938	0.836432	0.798516	0.804789
MDT	0.370139	0.392256	0.600765	0.491939	0.571048	0.114734	0.416064	0.729050	0.492226	0.674131	...	1.000000	0.462105	0.524977	0.768838	0.775133	0.764672	0.847653	0.820833	0.792783	0.805099
VEEV	0.307317	0.553329	0.397144	0.499758	0.463816	0.477715	0.491907	0.523549	0.517494	0.555986	...	0.462105	1.000000	0.574280	0.673689	0.697796	0.675546	0.665481	0.635087	0.619297	0.646572
WST	0.312676	0.503020	0.437905	0.479871	0.497678	0.315361	0.387376	0.571468	0.531894	0.595404	...	0.524977	0.574280	1.000000	0.676464	0.656592	0.664424	0.689419	0.660132	0.675793	0.652869
FSPHX	0.502238	0.604170	0.684917	0.622924	0.780172	0.382295	0.529738	0.774596	0.666729	0.794327	...	0.768838	0.673689	0.676464	1.000000	0.968210	0.988098	0.922114	0.963555	0.950730	0.903873
BHCFX	0.471266	0.591088	0.676603	0.624295	0.729304	0.392995	0.605759	0.787361	0.657117	0.787094	...	0.775133	0.697796	0.656592	0.968210	1.000000	0.975807	0.929828	0.944187	0.916014	0.924436
PRHSX	0.513622	0.600802	0.683284	0.627216	0.756161	0.396068	0.545473	0.788606	0.676330	0.806280	...	0.764672	0.675546	0.664424	0.988098	0.975807	1.000000	0.923149	0.963641	0.945235	0.910925
IHI	0.437217	0.547192	0.661016	0.588734	0.675662	0.323259	0.561182	0.785022	0.665213	0.845266	...	0.847653	0.665481	0.689419	0.922114	0.929828	0.923149	1.000000	0.945555	0.937979	0.933514
RYH	0.552704	0.583548	0.722278	0.632329	0.754814	0.320719	0.482979	0.791210	0.661519	0.811404	...	0.820833	0.635087	0.660132	0.963555	0.944187	0.963641	0.945555	1.000000	0.973813	0.919130
XLV	0.510095	0.573477	0.677864	0.597185	0.749223	0.292672	0.445336	0.746226	0.661616	0.830435	...	0.792783	0.619297	0.675793	0.950730	0.916014	0.945235	0.937979	0.973813	1.000000	0.865151
XHE	0.460672	0.547343	0.677175	0.605994	0.653368	0.380840	0.600303	0.802116	0.631179	0.757714	...	0.805099	0.646572	0.652869	0.903873	0.924436	0.910925	0.933514	0.919130	0.865151	1.000000

26 rows × 26 columns

normalized_rets = Normalized_Value.corr()
normalized_pair_value = normalized_rets.abs().unstack()
normalized_pair_value.sort_values(ascending = False)

XHE    XHE      1.000000
BMY    BMY      1.000000
IHI    IHI      1.000000
PRHSX  PRHSX    1.000000
BHCFX  BHCFX    1.000000
FSPHX  FSPHX    1.000000
WST    WST      1.000000
VEEV   VEEV     1.000000
MDT    MDT      1.000000
IQV    IQV      1.000000
IOVA   IOVA     1.000000
GH     GH       1.000000
ANTM   ANTM     1.000000
XLV    XLV      1.000000
TMO    TMO      1.000000
DHR    DHR      1.000000
BIO    BIO      1.000000
HOLX   HOLX     1.000000
DXCM   DXCM     1.000000
TDOC   TDOC     1.000000
CNC    CNC      1.000000
LHCG   LHCG     1.000000
EHC    EHC      1.000000
AMED   AMED     1.000000
RYH    RYH      1.000000
PRGO   PRGO     1.000000
FSPHX  PRHSX    0.988098
PRHSX  FSPHX    0.988098
BHCFX  PRHSX    0.975807
PRHSX  BHCFX    0.975807
                  ...   
IOVA   WST      0.269274
WST    IOVA     0.269274
IOVA   PRGO     0.266580
PRGO   IOVA     0.266580
TDOC   HOLX     0.258916
HOLX   TDOC     0.258916
CNC    IOVA     0.254692
IOVA   CNC      0.254692
CNC    TDOC     0.245909
TDOC   CNC      0.245909
GH     BMY      0.242537
BMY    GH       0.242537
TDOC   EHC      0.239568
EHC    TDOC     0.239568
GH     TDOC     0.210314
TDOC   GH       0.210314
GH     PRGO     0.201491
PRGO   GH       0.201491
TDOC   PRGO     0.185020
PRGO   TDOC     0.185020
BMY    TDOC     0.166238
TDOC   BMY      0.166238
       ANTM     0.140352
ANTM   TDOC     0.140352
IQV    TDOC     0.137542
TDOC   IQV      0.137542
DXCM   PRGO     0.137146
PRGO   DXCM     0.137146
MDT    TDOC     0.114734
TDOC   MDT      0.114734
Length: 676, dtype: float64

print("Stock returns: ")
print(rets.mean())
print('-' * 50)
print("Stock risks:")
print(rets.std())

Stock returns: 
PRGO    -0.000061
AMED     0.001703
EHC     -0.000206
LHCG     0.001689
CNC      0.000160
TDOC     0.002939
DXCM     0.003250
HOLX     0.000938
BIO      0.001113
DHR      0.001288
TMO      0.001059
ANTM     0.000328
BMY      0.000131
GH       0.003362
IOVA     0.003369
IQV      0.000517
MDT      0.000093
VEEV     0.002441
WST      0.001672
FSPHX    0.000654
BHCFX    0.000764
PRHSX    0.000559
IHI      0.000549
RYH      0.000339
XLV      0.000315
XHE      0.000247
dtype: float64
--------------------------------------------------
Stock risks:
PRGO     0.030528
AMED     0.029877
EHC      0.023028
LHCG     0.026895
CNC      0.029700
TDOC     0.038778
DXCM     0.034169
HOLX     0.022065
BIO      0.022474
DHR      0.018402
TMO      0.019889
ANTM     0.028287
BMY      0.018732
GH       0.049446
IOVA     0.053224
IQV      0.025556
MDT      0.020165
VEEV     0.028515
WST      0.020553
FSPHX    0.016666
BHCFX    0.016889
PRHSX    0.017368
IHI      0.017951
RYH      0.016676
XLV      0.015999
XHE      0.017789
dtype: float64

table = pd.DataFrame()
table['Returns'] = rets.mean()
table['Risk'] = rets.std()
table.sort_values(by='Returns')

	Returns	Risk
EHC	-0.000206	0.023028
PRGO	-0.000061	0.030528
MDT	0.000093	0.020165
BMY	0.000131	0.018732
CNC	0.000160	0.029700
XHE	0.000247	0.017789
XLV	0.000315	0.015999
ANTM	0.000328	0.028287
RYH	0.000339	0.016676
IQV	0.000517	0.025556
IHI	0.000549	0.017951
PRHSX	0.000559	0.017368
FSPHX	0.000654	0.016666
BHCFX	0.000764	0.016889
HOLX	0.000938	0.022065
TMO	0.001059	0.019889
BIO	0.001113	0.022474
DHR	0.001288	0.018402
WST	0.001672	0.020553
LHCG	0.001689	0.026895
AMED	0.001703	0.029877
VEEV	0.002441	0.028515
TDOC	0.002939	0.038778
DXCM	0.003250	0.034169
GH	0.003362	0.049446
IOVA	0.003369	0.053224

table.sort_values(by='Risk')

	Returns	Risk
XLV	0.000315	0.015999
FSPHX	0.000654	0.016666
RYH	0.000339	0.016676
BHCFX	0.000764	0.016889
PRHSX	0.000559	0.017368
XHE	0.000247	0.017789
IHI	0.000549	0.017951
DHR	0.001288	0.018402
BMY	0.000131	0.018732
TMO	0.001059	0.019889
MDT	0.000093	0.020165
WST	0.001672	0.020553
HOLX	0.000938	0.022065
BIO	0.001113	0.022474
EHC	-0.000206	0.023028
IQV	0.000517	0.025556
LHCG	0.001689	0.026895
ANTM	0.000328	0.028287
VEEV	0.002441	0.028515
CNC	0.000160	0.029700
AMED	0.001703	0.029877
PRGO	-0.000061	0.030528
DXCM	0.003250	0.034169
TDOC	0.002939	0.038778
GH	0.003362	0.049446
IOVA	0.003369	0.053224

rf = 0.01
table['Sharpe Ratio'] = (table['Returns'] - rf) / table['Risk']
table

	Returns	Risk	Sharpe Ratio
PRGO	-0.000061	0.030528	-0.329550
AMED	0.001703	0.029877	-0.277692
EHC	-0.000206	0.023028	-0.443192
LHCG	0.001689	0.026895	-0.309010
CNC	0.000160	0.029700	-0.331305
TDOC	0.002939	0.038778	-0.182088
DXCM	0.003250	0.034169	-0.197559
HOLX	0.000938	0.022065	-0.410700
BIO	0.001113	0.022474	-0.395423
DHR	0.001288	0.018402	-0.473425
TMO	0.001059	0.019889	-0.449534
ANTM	0.000328	0.028287	-0.341946
BMY	0.000131	0.018732	-0.526852
GH	0.003362	0.049446	-0.134253
IOVA	0.003369	0.053224	-0.124586
IQV	0.000517	0.025556	-0.371051
MDT	0.000093	0.020165	-0.491305
VEEV	0.002441	0.028515	-0.265104
WST	0.001672	0.020553	-0.405186
FSPHX	0.000654	0.016666	-0.560820
BHCFX	0.000764	0.016889	-0.546886
PRHSX	0.000559	0.017368	-0.543610
IHI	0.000549	0.017951	-0.526477
RYH	0.000339	0.016676	-0.579315
XLV	0.000315	0.015999	-0.605359
XHE	0.000247	0.017789	-0.548260

table['Max Returns'] = rets.max()

table['Min Returns'] = rets.min()

table['Median Returns'] = rets.median()

total_return = stock_rets[-1:].transpose()
table['Total Return'] = 100 * total_return
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return
PRGO	-0.000061	0.030528	-0.329550	0.138465	-0.292781	0.001090	1.445781
AMED	0.001703	0.029877	-0.277692	0.141639	-0.173284	0.003788	3.463538
EHC	-0.000206	0.023028	-0.443192	0.109984	-0.109981	0.000301	-0.591234
LHCG	0.001689	0.026895	-0.309010	0.112102	-0.190281	0.002478	3.202214
CNC	0.000160	0.029700	-0.331305	0.156024	-0.150071	0.000000	2.391725
TDOC	0.002939	0.038778	-0.182088	0.181318	-0.135371	0.002820	2.380591
DXCM	0.003250	0.034169	-0.197559	0.271552	-0.170970	0.002858	0.961199
HOLX	0.000938	0.022065	-0.410700	0.092626	-0.205274	0.001533	1.676917
BIO	0.001113	0.022474	-0.395423	0.171687	-0.074554	0.001435	1.114652
DHR	0.001288	0.018402	-0.473425	0.096982	-0.097249	0.001522	0.507937
TMO	0.001059	0.019889	-0.449534	0.104700	-0.082875	0.002349	1.092085
ANTM	0.000328	0.028287	-0.341946	0.156174	-0.171266	0.000472	1.434069
BMY	0.000131	0.018732	-0.526852	0.061207	-0.132641	0.000328	0.727148
GH	0.003362	0.049446	-0.134253	0.285754	-0.165394	0.001368	1.894919
IOVA	0.003369	0.053224	-0.124586	0.360641	-0.222753	0.002001	-3.764377
IQV	0.000517	0.025556	-0.371051	0.153691	-0.165382	0.001366	0.974574
MDT	0.000093	0.020165	-0.491305	0.101893	-0.128237	0.000910	0.577252
VEEV	0.002441	0.028515	-0.265104	0.147297	-0.126228	0.003163	1.071235
WST	0.001672	0.020553	-0.405186	0.101693	-0.107043	0.001156	3.163829
FSPHX	0.000654	0.016666	-0.560820	0.069865	-0.109649	0.001630	1.412715
BHCFX	0.000764	0.016889	-0.546886	0.083417	-0.116017	0.002096	1.537382
PRHSX	0.000559	0.017368	-0.543610	0.073700	-0.113336	0.001448	1.220934
IHI	0.000549	0.017951	-0.526477	0.108216	-0.105726	0.001749	0.793930
RYH	0.000339	0.016676	-0.579315	0.076366	-0.098705	0.001428	0.912249
XLV	0.000315	0.015999	-0.605359	0.077057	-0.098610	0.000967	0.982096
XHE	0.000247	0.017789	-0.548260	0.093513	-0.085537	0.001527	1.200605

table['Average Return Days'] = (1 + total_return)**(1 / days) - 1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days
PRGO	-0.000061	0.030528	-0.329550	0.138465	-0.292781	0.001090	1.445781	0.000016
AMED	0.001703	0.029877	-0.277692	0.141639	-0.173284	0.003788	3.463538	0.000038
EHC	-0.000206	0.023028	-0.443192	0.109984	-0.109981	0.000301	-0.591234	-0.000007
LHCG	0.001689	0.026895	-0.309010	0.112102	-0.190281	0.002478	3.202214	0.000035
CNC	0.000160	0.029700	-0.331305	0.156024	-0.150071	0.000000	2.391725	0.000026
TDOC	0.002939	0.038778	-0.182088	0.181318	-0.135371	0.002820	2.380591	0.000026
DXCM	0.003250	0.034169	-0.197559	0.271552	-0.170970	0.002858	0.961199	0.000011
HOLX	0.000938	0.022065	-0.410700	0.092626	-0.205274	0.001533	1.676917	0.000018
BIO	0.001113	0.022474	-0.395423	0.171687	-0.074554	0.001435	1.114652	0.000012
DHR	0.001288	0.018402	-0.473425	0.096982	-0.097249	0.001522	0.507937	0.000006
TMO	0.001059	0.019889	-0.449534	0.104700	-0.082875	0.002349	1.092085	0.000012
ANTM	0.000328	0.028287	-0.341946	0.156174	-0.171266	0.000472	1.434069	0.000016
BMY	0.000131	0.018732	-0.526852	0.061207	-0.132641	0.000328	0.727148	0.000008
GH	0.003362	0.049446	-0.134253	0.285754	-0.165394	0.001368	1.894919	0.000021
IOVA	0.003369	0.053224	-0.124586	0.360641	-0.222753	0.002001	-3.764377	-0.000042
IQV	0.000517	0.025556	-0.371051	0.153691	-0.165382	0.001366	0.974574	0.000011
MDT	0.000093	0.020165	-0.491305	0.101893	-0.128237	0.000910	0.577252	0.000006
VEEV	0.002441	0.028515	-0.265104	0.147297	-0.126228	0.003163	1.071235	0.000012
WST	0.001672	0.020553	-0.405186	0.101693	-0.107043	0.001156	3.163829	0.000034
FSPHX	0.000654	0.016666	-0.560820	0.069865	-0.109649	0.001630	1.412715	0.000016
BHCFX	0.000764	0.016889	-0.546886	0.083417	-0.116017	0.002096	1.537382	0.000017
PRHSX	0.000559	0.017368	-0.543610	0.073700	-0.113336	0.001448	1.220934	0.000013
IHI	0.000549	0.017951	-0.526477	0.108216	-0.105726	0.001749	0.793930	0.000009
RYH	0.000339	0.016676	-0.579315	0.076366	-0.098705	0.001428	0.912249	0.000010
XLV	0.000315	0.015999	-0.605359	0.077057	-0.098610	0.000967	0.982096	0.000011
XHE	0.000247	0.017789	-0.548260	0.093513	-0.085537	0.001527	1.200605	0.000013

initial_value = df.iloc[0]
ending_value = df.iloc[-1]
table['CAGR'] = ((ending_value / initial_value) ** (252.0 / days)) -1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
PRGO	-0.000061	0.030528	-0.329550	0.138465	-0.292781	0.001090	1.445781	0.000016	-0.119840
AMED	0.001703	0.029877	-0.277692	0.141639	-0.173284	0.003788	3.463538	0.000038	0.438768
EHC	-0.000206	0.023028	-0.443192	0.109984	-0.109981	0.000301	-0.591234	-0.000007	0.057836
LHCG	0.001689	0.026895	-0.309010	0.112102	-0.190281	0.002478	3.202214	0.000035	0.331087
CNC	0.000160	0.029700	-0.331305	0.156024	-0.150071	0.000000	2.391725	0.000026	0.060585
TDOC	0.002939	0.038778	-0.182088	0.181318	-0.135371	0.002820	2.380591	0.000026	0.613337
DXCM	0.003250	0.034169	-0.197559	0.271552	-0.170970	0.002858	0.961199	0.000011	0.709864
HOLX	0.000938	0.022065	-0.410700	0.092626	-0.205274	0.001533	1.676917	0.000018	0.069503
BIO	0.001113	0.022474	-0.395423	0.171687	-0.074554	0.001435	1.114652	0.000012	0.179848
DHR	0.001288	0.018402	-0.473425	0.096982	-0.097249	0.001522	0.507937	0.000006	0.193549
TMO	0.001059	0.019889	-0.449534	0.104700	-0.082875	0.002349	1.092085	0.000012	0.184277
ANTM	0.000328	0.028287	-0.341946	0.156174	-0.171266	0.000472	1.434069	0.000016	0.049712
BMY	0.000131	0.018732	-0.526852	0.061207	-0.132641	0.000328	0.727148	0.000008	0.006490
GH	0.003362	0.049446	-0.134253	0.285754	-0.165394	0.001368	1.894919	0.000021	NaN
IOVA	0.003369	0.053224	-0.124586	0.360641	-0.222753	0.002001	-3.764377	-0.000042	0.407016
IQV	0.000517	0.025556	-0.371051	0.153691	-0.165382	0.001366	0.974574	0.000011	0.094120
MDT	0.000093	0.020165	-0.491305	0.101893	-0.128237	0.000910	0.577252	0.000006	0.037904
VEEV	0.002441	0.028515	-0.265104	0.147297	-0.126228	0.003163	1.071235	0.000012	0.501070
WST	0.001672	0.020553	-0.405186	0.101693	-0.107043	0.001156	3.163829	0.000034	0.256989
FSPHX	0.000654	0.016666	-0.560820	0.069865	-0.109649	0.001630	1.412715	0.000016	0.122009
BHCFX	0.000764	0.016889	-0.546886	0.083417	-0.116017	0.002096	1.537382	0.000017	NaN
PRHSX	0.000559	0.017368	-0.543610	0.073700	-0.113336	0.001448	1.220934	0.000013	0.092186
IHI	0.000549	0.017951	-0.526477	0.108216	-0.105726	0.001749	0.793930	0.000009	0.113707
RYH	0.000339	0.016676	-0.579315	0.076366	-0.098705	0.001428	0.912249	0.000010	0.055746
XLV	0.000315	0.015999	-0.605359	0.077057	-0.098610	0.000967	0.982096	0.000011	0.061239
XHE	0.000247	0.017789	-0.548260	0.093513	-0.085537	0.001527	1.200605	0.000013	0.082812

table.sort_values(by='Average Return Days')

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
IOVA	0.003369	0.053224	-0.124586	0.360641	-0.222753	0.002001	-3.764377	-0.000042	0.407016
EHC	-0.000206	0.023028	-0.443192	0.109984	-0.109981	0.000301	-0.591234	-0.000007	0.057836
DHR	0.001288	0.018402	-0.473425	0.096982	-0.097249	0.001522	0.507937	0.000006	0.193549
MDT	0.000093	0.020165	-0.491305	0.101893	-0.128237	0.000910	0.577252	0.000006	0.037904
BMY	0.000131	0.018732	-0.526852	0.061207	-0.132641	0.000328	0.727148	0.000008	0.006490
IHI	0.000549	0.017951	-0.526477	0.108216	-0.105726	0.001749	0.793930	0.000009	0.113707
RYH	0.000339	0.016676	-0.579315	0.076366	-0.098705	0.001428	0.912249	0.000010	0.055746
DXCM	0.003250	0.034169	-0.197559	0.271552	-0.170970	0.002858	0.961199	0.000011	0.709864
IQV	0.000517	0.025556	-0.371051	0.153691	-0.165382	0.001366	0.974574	0.000011	0.094120
XLV	0.000315	0.015999	-0.605359	0.077057	-0.098610	0.000967	0.982096	0.000011	0.061239
VEEV	0.002441	0.028515	-0.265104	0.147297	-0.126228	0.003163	1.071235	0.000012	0.501070
TMO	0.001059	0.019889	-0.449534	0.104700	-0.082875	0.002349	1.092085	0.000012	0.184277
BIO	0.001113	0.022474	-0.395423	0.171687	-0.074554	0.001435	1.114652	0.000012	0.179848
XHE	0.000247	0.017789	-0.548260	0.093513	-0.085537	0.001527	1.200605	0.000013	0.082812
PRHSX	0.000559	0.017368	-0.543610	0.073700	-0.113336	0.001448	1.220934	0.000013	0.092186
FSPHX	0.000654	0.016666	-0.560820	0.069865	-0.109649	0.001630	1.412715	0.000016	0.122009
ANTM	0.000328	0.028287	-0.341946	0.156174	-0.171266	0.000472	1.434069	0.000016	0.049712
PRGO	-0.000061	0.030528	-0.329550	0.138465	-0.292781	0.001090	1.445781	0.000016	-0.119840
BHCFX	0.000764	0.016889	-0.546886	0.083417	-0.116017	0.002096	1.537382	0.000017	NaN
HOLX	0.000938	0.022065	-0.410700	0.092626	-0.205274	0.001533	1.676917	0.000018	0.069503
GH	0.003362	0.049446	-0.134253	0.285754	-0.165394	0.001368	1.894919	0.000021	NaN
TDOC	0.002939	0.038778	-0.182088	0.181318	-0.135371	0.002820	2.380591	0.000026	0.613337
CNC	0.000160	0.029700	-0.331305	0.156024	-0.150071	0.000000	2.391725	0.000026	0.060585
WST	0.001672	0.020553	-0.405186	0.101693	-0.107043	0.001156	3.163829	0.000034	0.256989
LHCG	0.001689	0.026895	-0.309010	0.112102	-0.190281	0.002478	3.202214	0.000035	0.331087
AMED	0.001703	0.029877	-0.277692	0.141639	-0.173284	0.003788	3.463538	0.000038	0.438768